h2o4gpu.pca {h2o4gpu} | R Documentation |
Principal Component Analysis (PCA)
Description
Principal Component Analysis (PCA)
Usage
h2o4gpu.pca(n_components = 2L, copy = TRUE, whiten = FALSE,
svd_solver = "arpack", tol = 0, iterated_power = "auto",
random_state = NULL, verbose = FALSE, backend = "h2o4gpu",
gpu_id = 0L)
Arguments
n_components |
Desired dimensionality of output data |
copy |
If FALSE, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead. |
whiten |
When TRUE (FALSE by default) the |
svd_solver |
'auto' is selected by a default policy based on |
tol |
Tolerance for singular values computed by svd_solver == 'arpack'. |
iterated_power |
Number of iterations for the power method computed by svd_solver == 'randomized'. |
random_state |
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If NULL, the random number generator is the RandomState instance used by |
verbose |
Verbose or not |
backend |
Which backend to use. Options are 'auto', 'sklearn', 'h2o4gpu'. Saves as attribute for actual backend used. |
gpu_id |
ID of the GPU on which the algorithm should run. Only used by h2o4gpu backend. |